Segmentation of structures in clinical images is a precursor to computer-aided detection (CAD) for many musculoskeletal pathologies. Accurate CAD systems could considerably improve the efficiency and objectivity of radiological practice by providing clinicians with image-based biomarkers calculated with minimal human input. However, such systems rarely achieve human-level performance, so extensive manual checking may be required. Their practical utility could therefore be increased by accurate error estimation, focusing manual input on the images or structures where it is needed. Standard techniques such as the minimum variance bound can estimate random errors, but provide no way to estimate any systematic errors due to model fitting failure. We describe the use of multiple, independent sub-models to estimate both systematic and random errors. The approach is evaluated on vertebral body segmentation in lateral spinal images, demonstrating large (up to 50%) and significant improvements in the accuracy of error classification with concurrent improvements in annotation accuracy. Whilst further work is required to elucidate the definition of “independence” in this context, we conclude that the approach provides a valuable component for appearance model based CAD systems.