Accurate crack diagnosis in multi-fastener metallic structures is critical for instructing aircraft structural ground tests and ensuring in-service safety. However, heteroscedastic uncertainties in the relationship between crack length and guided-wave damage indices severely compromise diagnostic accuracy and reliability assessment. To address this, a multi-fastener-joint crack diagnosis method based on Quantile Regression Neural Network (QRNN) is proposed. The QRNN establishes a mapping model between guided-wave damage index and the crack length, where crack diagnosis result is determined through the median quantile point. Furthermore, by comprehensively leveraging the quantile outputs, the diagnostic reliability across different crack lengths is quantitatively characterized. A complex multi-layer stringer structure with multiple fastener joints was adopted as the testbed to validate the diagnostic capability and reliability assessment. Experimental results indicate that the proposed approach enables precise crack diagnosis in characteristic longeron fastenerjoint areas, exhibiting Root Mean Squared Error (RMSE) 1.2 mm in the skin and RMSE of 2.2 mm in the stringer, with concurrent quantification of diagnostic reliability.