The sample selection approach is a widely adopted strategy for learning with noisy labels, where examples with lower losses are effectively treated as clean during training. However, this clean set often becomes dominated by easy examples, limiting the model’s meaningful exposure to more challenging cases and reducing its expressive power. To overcome this limitation, we introduce a novel metric called Dynamic Center Distance (DCD), which can quantify sample difficulty and provide information that critically complements loss values. Unlike approaches that rely on predictions, DCD is computed in feature space as the distance between sample features and a dynamically updated center, established through a proposed meta-learning framework. Building on preliminary semi-supervised training that captures fundamental data patterns, we incorporate DCD to further refine the classification loss, down-weighting well-classified examples and strategically focusing training on a sparse set of hard instances. This strategy prevents easy examples from dominating the classifier, leading to more robust learning. Extensive experiments across multiple benchmark datasets, including synthetic and real-world noise settings, as well as natural and medical images, consistently demonstrate the effectiveness of our method.