Stereoscopic reconstruction is important to automatic vision systems. As an intermediate step, estimating this reconstruction is not enough for good performance of the whole system, and its uncertainty must be characterized. Several methods propose uncertainty indexes based on specific data features, thus incomplete, while others are based on learning. We propose a simple index, named ambiguity index, taking into account both data and regularization, and derived directly from the optimization process. Exploiting properties of dynamic programming, this index is related to the posterior variance of the solution when the Semi-Global Matching (SGM) algorithm is used for stereo reconstruction. To illustrate its interest, improvements in refining stereo reconstruction are shown on the KITTI datasets when the index is used.