Purpose: Quantitative tools to estimate the uncertainty in deformable image registration are lacking. Distance discordance(DD) is a quantity that we have defined, and is an estimate of the mean error (measured in terms of distance) due to deformable image registration. This abstract presents a refined method of estimating distance discordance that improves accuracy. Methods: Starting from an arbitrary reference image [i] in a set of images [1…n], voxels are traced from any two moving images [j] and [k] that happened to be registered at the same voxel on [i] to other reference images in the image set. The difference between each pair of points is recorded in the Cartesian coordinate system (dx,dy,dz). The mean of the difference (<dx>, <dy>, <dz>) is subtracted from the original difference in order to calculate the unbiased distance between these pair of points and which corresponded to distance discordance metric. Experiments using this new approach were evaluated in a set of 8 different head and neck patients. Results: The 3D spatial map of the mean DD metric showed a variation among anatomical sites in the head and neck region. Regions of high contrast (bones) showed a lower mean DD value (2—13 mm) while regions of low contrast (soft tissue) had a larger mean DD value (6—19 mm). The shape of the dose discordance histogram (DDH) for some anatomical structures followed a log normal distribution with a long tail while others had irregular shape. Conclusion: The updated DD metric gives an unbiased measure of the spatial uncertainty in image registration in the absence of a ground truth. For instance, the DD maps can be used to set a level of confidence for dose accumulation in adaptive radiotherapy. However, the DD metric provides a necessary but not a sufficient condition to ensure good registration.