  We address the selection and evaluation of uncertain segmentation methods in
medical imaging and present two case studies: prostate segmentation,
illustrating that for minimal annotator variation simple deterministic models
can suffice, and lung lesion segmentation, highlighting the limitations of the
Generalized Energy Distance (GED) in model selection. Our findings lead to
guidelines for accurately choosing and developing uncertain segmentation
models, that integrate aleatoric and epistemic components. These guidelines are
designed to aid researchers and practitioners in better developing, selecting,
and evaluating uncertain segmentation methods, thereby facilitating enhanced
adoption and effective application of segmentation uncertainty in practice.
