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

ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation