High-precision stereo matching implementation requires substantial computational resources. To achieve precise real-time depth estimation on embedded devices, we propose a hardware accelerator for local-expansion-move (LE) that can be utilized for accurate disparity extraction. By inferring per-pixel 3D plane labels, it is able to obtain smooth disparities. With the efficient multi-level pipeline design and sophisticated memory structures, we have implemented two key techniques, localization and spatial propagation, which significantly enhance the efficiency and precision of the inference. Compared to the software implementations of LE on CPU and GPU, the accelerator exhibits speedups of 524x and 27.5x respectively. It achieves state-of-the-art matching accuracy in embedded systems by integrating with a lightweight neural network, while maintaining quasi-real-time efficiency. Moreover, the accelerator is implemented using high-level synthesis, which allows for easy modification of accelerator parameters based on the image resolutions and performance requirements in different applications.