  Detecting out-of-distribution (OOD) samples for trusted medical image
segmentation remains a significant challenge. The critical issue here is the
lack of a strict definition of abnormal data, which often results in artificial
problem settings without measurable clinical impact. In this paper, we redesign
the OOD detection problem according to the specifics of volumetric medical
imaging and related downstream tasks (e.g., segmentation). We propose using the
downstream model's performance as a pseudometric between images to define
abnormal samples. This approach enables us to weigh different samples based on
their performance impact without an explicit ID/OOD distinction. We incorporate
this weighting in a new metric called Expected Performance Drop (EPD). EPD is
our core contribution to the new problem design, allowing us to rank methods
based on their clinical impact. We demonstrate the effectiveness of EPD-based
evaluation in 11 CT and MRI OOD detection challenges.
