Deep learning encounters significant challenges in the form of noisy-label samples, which can cause the overfitting of trained models. A primary challenge in learning with noisy-label (LNL) techniques is their ability to differentiate between hard samples (clean-label samples near the decision boundary) and instance-dependent noisy (IDN) label samples to allow these samples to be treated differently during training. Existing methodologies to identify IDN samples, including the small-loss hypothesis and feature-based selection, have demonstrated limited efficacy, thus impeding their effectiveness in dealing with real-world label noise. We present Peer-Agreement-based Sample Selection (PASS), a novel approach that utilises three classifiers, where a consensus-driven agreement between two models accurately differentiates between clean and noisy-label IDN samples to train the third model. In contrast to current techniques, PASS is specifically designed to address the complexities of IDN, where noise patterns are correlated with instance features. Our approach seamlessly integrates with existing LNL algorithms to enhance the accuracy of detecting both noisy and clean samples. Comprehensive experiments conducted on simulated benchmarks (CIFAR-100 and Red mini-ImageNet) and real-world datasets (Animal-10N, CIFAR-N, Clothing1M, and mini-WebVision) demonstrated that PASS substantially improved the performance of multiple state-of-the-art methods. This technique achieves superior classification accuracy, particularly in scenarios with high noise levels.