  Probabilistic image segmentation encodes varying prediction confidence and
inherent ambiguity in the segmentation problem. While different probabilistic
segmentation models are designed to capture different aspects of segmentation
uncertainty and ambiguity, these modelling differences are rarely discussed in
the context of applications of uncertainty. We consider two common use cases of
segmentation uncertainty, namely assessment of segmentation quality and active
learning. We consider four established strategies for probabilistic
segmentation, discuss their modelling capabilities, and investigate their
performance in these two tasks. We find that for all models and both tasks,
returned uncertainty correlates positively with segmentation error, but does
not prove to be useful for active learning.
