The performance of Deep Learning (DL) segmentation algorithms is routinely determined using quantitative metrics like the Dice score and Hausdorff distance. However, these metrics show a low concordance with humans perception of segmentation quality. The successful collaboration of health care professionals with DL segmentation algorithms will require a detailed understanding of experts assessment of segmentation quality. Here, we present the results of a study on expert quality perception of brain tumor segmentations of brain MR images generated by a DL segmentation algorithm. Eight expert medical professionals were asked to grade the quality of segmentations on a scale from 1 (worst) to 4 (best). To this end, we collected four ratings for a dataset of 60 cases. We observed a low inter-rater agreement among all raters (Krippendorff s alpha: 0.34), which potentially is a result of different internal cutoffs for the quality ratings. Several factors, including the volume of the segmentation and model uncertainty, were associated with high disagreement between raters. Furthermore, the correlations between the ratings and commonly used quantitative segmentation quality metrics ranged from no to moderate correlation. We conclude that, similar to the inter-rater variability observed for manual brain tumor segmentation, segmentation quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences. Clearer guidelines for quality evaluation could help to mitigate these differences. Importantly, existing technical metrics do not capture clinical perception of segmentation quality. A better understanding of expert quality perception is expected to support the design of more human-centered DL algorithms for integration into the clinical workflow.