Domain adaptive panoptic segmentation promises to resolve the long tail of corner cases in natural scene understanding. Most approaches involve consistency learning with Mean Teacher. Previous state of the art extends this baseline with cross-task consistency, careful system-level optimization and heuristic improvement of teacher predictions. In contrast, we propose to build upon remarkable capability of mask transformers to estimate their own prediction uncertainty. Our method favours training on confident pseudo-labels by leveraging fine-grained confidence of panoptic teacher predictions. In particular, we modulate the loss with mask-wide confidence and discourage back-propagation in pixels with uncertain mask assignment. Experimental evaluation on standard benchmarks reveals a substantial contribution of the proposed selection techniques. We report 47.4 PQ on Synthia to Citysapes which corresponds to an improvement of 6.2 percentage points over the state of the art.