The patch-match algorithm has been widely used in binocular stereo reconstruction due to its low memory consumption and high reconstruction accuracy. However, the traditional patch-match algorithm needs to iteratively calculate the optimal disparity d for each pixel of image in an orderly manner, which resulting in a high running time. In order to solve this problem, a learning-based model on the basis of traditional patch-match algorithm as a guide to reduce the running time and improve the accuracy of stereo reconstruction was introduced. First, the deep learning model was used to output the initial disparity map of each pixel with heteroscedastic uncertainty, which was used to measure the accuracy of the disparity predicted by the network model. Then, the heteroscedastic uncertainty and initial disparity were taken as the prior information of patch-match algorithm. Finally, in the plane refinement step, the heteroscedastic uncertainty of each pixel was used to dynamically adjust its search interval to achieve the goal of reducing the running time. On the Middlebury dataset, compared with the original algorithm, the running time of the improved algorithm is reduced by 20%, and the reconstruction accuracy of the discontinuous region is slightly improved.