  Probabilistic convolutional neural networks, which predict distributions of
predictions instead of point estimates, led to recent advances in many areas of
computer vision, from image reconstruction to semantic segmentation. Besides
state of the art benchmark results, these networks made it possible to quantify
local uncertainties in the predictions. These were used in active learning
frameworks to target the labeling efforts of specialist annotators or to assess
the quality of a prediction in a safety-critical environment. However, for
instance segmentation problems these methods are not frequently used so far. We
seek to close this gap by proposing a generic method to obtain model-inherent
uncertainty estimates within proposal-free instance segmentation models.
Furthermore, we analyze the quality of the uncertainty estimates with a metric
adapted from semantic segmentation. We evaluate our method on the BBBC010 C.\
elegans dataset, where it yields competitive performance while also predicting
uncertainty estimates that carry information about object-level inaccuracies
like false splits and false merges. We perform a simulation to show the
potential use of such uncertainty estimates in guided proofreading.
