  Segmentation is a fundamental task in medical image analysis. The clinical
interest is often to measure the volume of a structure. To evaluate and compare
segmentation methods, the similarity between a segmentation and a predefined
ground truth is measured using metrics such as the Dice score. Recent
segmentation methods based on convolutional neural networks use a
differentiable surrogate of the Dice score, such as soft Dice, explicitly as
the loss function during the learning phase. Even though this approach leads to
improved Dice scores, we find that, both theoretically and empirically on four
medical tasks, it can introduce a volumetric bias for tasks with high inherent
uncertainty. As such, this may limit the method's clinical applicability.
