In this paper we present a novel post-processing technique to detect and correct inconsistency-based errors in non-rigid registration. While deformable registration is ubiquitous in medical image computing, assessing its quality has yet been an open problem. We propose a method that predicts local registration errors of existing pairwise registrations between a set of images, while simultaneously estimating corrected registrations. In the solution the error is constrained to be small in areas of high post-registration image similarity, while local registrations are constrained to be consistent between direct and indirect registration paths. The latter is a critical property of an ideal registration process, and has been frequently used to asses the performance of registration algorithms. In our work, the consistency is used as a target criterion, for which we efficiently find a solution using a linear least-squares model on a coarse grid of registration control points. We show experimentally that the local errors estimated by our algorithm correlate strongly with true registration errors in experiments with known, dense ground-truth deformations. Additionally, the estimated corrected registrations consistently improve over the initial registrations in terms of average deformation error or TRE for different registration algorithms on both simulated and clinical data, independent of modality (MRI/CT), dimensionality (2D/3D) and employed primary registration method (demons/Markov-randomfield).