Retrospective studies using electronic health records (EHRs) often require diagnosis labels that reflect whether treating physicians considered a disease to be present. Such physician-determined diagnosis (PDD) labels are expensive to obtain through expert review, while available alternatives provide only noisy proxies. We exploit two complementary proxy labels: administrative diagnostic codes and free-text disease listings in discharge summaries. We introduce a dual-noisy-label learning framework that models PDD as a latent variable and treats these proxies as noisy observations of it, allowing a pretrained language model to learn from their agreement and disagreement without expert-labeled training data. We evaluate the approach on extracting Acute Kidney Injury (AKI) PDD from MIMIC-IV discharge summaries. Based on an expert-adjudicated evaluation set of 150 summaries, our PLM-PDD model achieves 94.6% population-weighted accuracy, compared with 89.4% for the same model trained using administrative coding, and 85.9% and 78.0% for AKI proxies. Largest gains occur in the gray-zone, where the proxies disagree. Ablation experiments show that gray-zone examples and auxiliary ICD supervision contribute to performance, while expert-highlighted evidence receives substantially higher model attention than matched random text. These results demonstrate that complementary noisy EHR labels can be used to learn clinically meaningful diagnosis labels without requiring expert annotation.