Aiming to address the repetitive need for complex calibration in existing snapshot spectral imaging methods, while better trading off system complexity and spectral reconstruction accuracy, we demonstrate a novel Parallel Coded Calibration-free Aperture Diffraction Imaging Spectrometer (PCCADIS) with minimal parallel complexity and enhanced light throughput. The system integrates monochromatic acquisition of diffraction-induced blurred spatial-spectral projections with uncalibrated chromatic filtering for guidance, enabling portable and lightweight implementation. In the inverse process of PCCADIS, aperture diffraction produces blurred patterns through global replication and band-by-band integration, posing challenges in registration with clearly structured RGB information by feature matching. Therefore, the Self-aware Spectral Fusion Cascaded Transformer (SSFCT) is proposed to realize the fusion and reconstruction of uncalibrated input, which demonstrates the potential to substantially improve the accuracy of snapshot spectral imaging while concurrently reducing the associated costs. Our methodology is rigorously evaluated through comprehensive simulation experiments and real reconstruction experiments with a prototype, confirming the high accuracy and user-friendliness of the PCCADIS.