Although learning-based stereo matching methods have achieved remarkable performance, accurately recovering disparity maps for boundary areas (e.g., thin structures) remains an intractable issue. Existing stereo pipelines usually employ the standard L1 loss function, averaging pixel-wise losses equally despite variable difficulty. This mechanism inevitably overwhelms thin structures due to their limited proportion. In this paper, we propose reweighting the L1 loss to focus on pixels of varying hardness. First, we explicitly model the uncertainty of each pixel to gauge the confidence in its prediction. By aggregating the volume, uncertainty is obtained effortlessly. Second, uncertainty is mapped to weights, which reweight the L1 loss accordingly. The core of our approach lies in leveraging uncertainty to personalize the loss map adjustment, progressively optimizing challenging regions during training. Notably, our method requires no extra parameters or inference computations. Finally, we introduce the Boundary Pixel Error (BPE), a novel metric targeting boundary quality. Extensive experiments with the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate the effectiveness and universality of our elegant framework, seamlessly integrating it into existing models as a plug-and-play component, leading to substantial performance improvements.