Accurate 3D microscopy image segmentation is essential for quantitative bioimage analysis, yet state-of-the-art foundation models frequently produce error-prone results that necessitate manual proofreading. Manual curation remains the bottleneck for generating high-quality training data and ensuring biological downstream accuracy. We present VessQC, an open-source tool for uncertainty-guided curation of volumetric segmentations. VessQC integrates uncertainty maps to prioritize user attention on regions with high error probability, optimizing the human-in-the-loop workflow. In a study of 3D light-sheet microscopy volumes of murine brain vasculature, uncertainty-guided correction improved error detection recall significantly compared to conventional curation, without increasing total processing time. VessQC thus enables efficient, human-in-the-loop refinement of volumetric segmentations and bridges a key gap in real-world applications between uncertainty estimation and practical human-computer interaction. The software is freely available at github.com/MMV-Lab/VessQC.