RAML (Reliability-Aware Mutual Learning) is a novel semi-supervised learning framework designed for accurate and robust medical image segmentation, particularly for acute ischemic stroke (AIS) lesion segmentation. By addressing key challenges in SSL including pseudo-label noise and network homogenization, RAML achieves state-of-the-art performance on multiple medical imaging datasets.