Automated brain segmentation approaches are increasingly being used for decision support in routine clinical settings. While segmentation may be considered a “solved problem” in research, it is still challenging to assure reliable performance of automated tools in clinical settings, which is a crucial requirement for diagnostic tools. To ensure correct results, automated quality control procedures are of vital importance, but they are often difficult to implement or time-consuming to run. We propose a simple and fast fully automated method to detect segmentation errors, and we evaluate its performance to detect skull-stripping-errors using results of two different brain segmentation algorithms on a large multicenter dataset. Results show that the method is able to detect skull-stripping-errors with high specificity.