Noisy labels in high-dimensional, and multiclass medical image datasets pose a significant challenge for machine learning models. While hybrid quantum-classical architectures, such as quantum neural networks (QNNs), have shown promise in medical imaging, their robustness under noisy label conditions remains largely unexplored. To address this gap, we propose a Noise-aware Quantum Neural Network (NQNN), integrating Fourier Attenuation, Reweight Estimation, and Adaptive Pooling to enhance feature extraction and classification robustness. Fourier Attenuation filters high-frequency noise, Reweight Estimation prioritizes cleaner labels based on uncertainty, and Adaptive Pooling dynamically refines feature aggregation. We evaluate NQNN on six benchmark medical datasets (PathMNIST, BloodMNIST, OrganAMNIST, OrganCMNIST, OCTMNIST, and DermaMNIST) across noise ratios (10%, 30%, and 50%) and classification configurations (binary, four-class, and full multiclass). Comparative benchmarks against five QNN-based and two deep-learning baselines demonstrate NQNN’s superior performance, such as achieving 80.25% accuracy on organCMNIST at 10% noise and maintaining strong performance at higher noise ratios. Our ablation studies validate the effectiveness of each noise-handling mechanism, highlighting their complementary contributions to noise robustness. By bridging quantum advancements with real-world medical diagnostics, NQNN establishes a new benchmark for noise-resilient medical image classification, offering a scalable and adaptive quantum-classical learning framework.