The outlines of target structures on medical imaging can be highly ambiguous. The uncertainty about the “true” outline is evident in high inter-rater variability of manual segmentations. So far, no method is available to identify cases likely to exhibit a high inter-rater variability. Here, we demonstrate that ground truth independent uncertainty metrics extracted from a MC dropout segmentation model developed on labels of only one rater correlate with inter-rater variability. This relationship can be used to identify ambiguous cases and flag them for more detailed review supporting consistent and reliable patient evaluation in research and clinical settings.
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