The primary challenge in deploying Human Pose Estimation (HPE) methods in real-world applications lies in balancing computational speed, model compactness, and prediction accuracy. Existing methods achieve strong performance in one or two aspects, but usually at the expense of the remaining one. To overcome this trade-off, we propose KeyRegionPose, a novel framework that achieves high accuracy while reducing model size and computational cost. Central to our design is the Region Focus Mechanism, which enables the model to concentrate on keypoint-relevant regions rather than the entire image. During training, we generate intermediate keypoint proposals to estimate keypoint-specific areas, from which the model learns region-focused features and refines predictions. To ensure accurate keypoint localization and enhance final pose estimation performance, we introduce a Cross-Representation Consistency Loss (CRC Loss) that enforces alignment between the predicted heatmaps and the regressed keypoint coordinates. Additionally, we propose Progressive Multi-Scale Token Pruning (PMTP), a strategy that prunes irrelevant tokens across multiple feature scales to accelerate inference. KeyRegionPose achieves 76.0 AP on the COCO validation set and 75.4 AP on the test-dev set, with only 20.0 million parameters and 8.6 GFLOPs—representing a 27.3% reduction in parameter count, 21.8% decrease in GFLOPs, and a competitive result (+0.2%) over state-of-the-art lightweight HPE models.