  We introduce ProPanDL, a family of networks capable of uncertainty-aware
panoptic segmentation. Unlike existing segmentation methods, ProPanDL is
capable of estimating full probability distributions for both the semantic and
spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL
variants capable of estimating both parametric (Variance Network) and
parameter-free (SampleNet) distributions quantifying pixel-wise spatial
uncertainty. We couple these approaches with two methods (Temperature Scaling
and Evidential Deep Learning) for semantic uncertainty estimation. To evaluate
the uncertainty-aware panoptic segmentation task, we address limitations with
existing approaches by proposing new metrics that enable separate evaluation of
spatial and semantic uncertainty. We additionally propose the use of the energy
score, a proper scoring rule, for more robust evaluation of spatial output
distributions. Using these metrics, we conduct an extensive evaluation of
ProPanDL variants. Our results demonstrate that ProPanDL is capable of
estimating well-calibrated and meaningful output distributions while still
retaining strong performance on the base panoptic segmentation task.
