diff --git a/src/torchmetrics/segmentation/hausdorff_distance.py b/src/torchmetrics/segmentation/hausdorff_distance.py
index 8b52d4bcb8c..d38b5c3b35f 100644
--- a/src/torchmetrics/segmentation/hausdorff_distance.py
+++ b/src/torchmetrics/segmentation/hausdorff_distance.py
@@ -16,6 +16,7 @@
 
 from torchmetrics.functional.segmentation import hausdorff_distance
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.data import dim_zero_cat
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
 
@@ -89,7 +90,9 @@ def update(self, preds: Tensor, target: Tensor) -> None:
 
     def compute(self) -> Tensor:
         """Compute final Hausdorff distance over states."""
-        return hausdorff_distance(self.preds, self.target, self.distance_metric, self.spacing)
+        return hausdorff_distance(
+            dim_zero_cat(self.preds), dim_zero_cat(self.target), self.distance_metric, self.spacing
+        )
 
     def plot(
         self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
diff --git a/tests/unittests/segmentation/inputs.py b/tests/unittests/segmentation/inputs.py
index a276876a06d..80d0a240d29 100644
--- a/tests/unittests/segmentation/inputs.py
+++ b/tests/unittests/segmentation/inputs.py
@@ -23,7 +23,6 @@
 # extrinsic input for clustering metrics that requires predicted clustering labels and target clustering labels
 Input = namedtuple("Input", ["preds", "target"])
 
-
 preds = torch.tensor(
     [
         [[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]],
diff --git a/tests/unittests/segmentation/test_hausdorff_distance.py b/tests/unittests/segmentation/test_hausdorff_distance.py
index 03eb6b20517..ee390a95c57 100644
--- a/tests/unittests/segmentation/test_hausdorff_distance.py
+++ b/tests/unittests/segmentation/test_hausdorff_distance.py
@@ -11,9 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from functools import partial
-
 import pytest
+import torch
 from skimage.metrics import hausdorff_distance as skimage_hausdorff_distance
 from torchmetrics.functional.segmentation.hausdorff_distance import hausdorff_distance
 from torchmetrics.segmentation.hausdorff_distance import HausdorffDistance
@@ -48,7 +47,7 @@ def test_hausdorff_distance(self, preds, target, distance_metric, ddp):
             preds=preds,
             target=target,
             metric_class=HausdorffDistance,
-            reference_metric=partial(skimage_hausdorff_distance, method="standard"),
+            reference_metric=skimage_hausdorff_distance,
             metric_args={"distance_metric": distance_metric, "spacing": None},
         )
 
@@ -58,9 +57,8 @@ def test_hausdorff_distance_functional(self, preds, target, distance_metric):
             preds=preds,
             target=target,
             metric_functional=hausdorff_distance,
-            reference_metric=partial(skimage_hausdorff_distance, method="standard"),
-            distance_metric=distance_metric,
-            spacing=None,
+            reference_metric=skimage_hausdorff_distance,
+            metric_args={"distance_metric": distance_metric, "spacing": None},
         )
 
 
@@ -69,3 +67,16 @@ def test_hausdorff_distance_functional_raises_invalid_task():
     preds, target = _inputs
     with pytest.raises(ValueError, match=r"Expected *"):
         hausdorff_distance(preds, target)
+
+
+@pytest.mark.parametrize(
+    "distance_metric",
+    ["euclidean", "chessboard", "taxicab"],
+)
+def test_hausdorff_distance_is_symmetric(distance_metric):
+    """Check that the metric functional is symmetric."""
+    for p, t in zip(_inputs.preds, _inputs.target):
+        assert torch.allclose(
+            hausdorff_distance(p, t, distance_metric),
+            hausdorff_distance(t, p, distance_metric),
+        )