AI-driven approaches have been proposed for pleural plaque (PP) segmentation from computed tomography (CT) scans, aiming to produce voxel-wise binary masks. While these models show strong potential for reproducible PP segmentation, they often struggle to capture small, thin, or morphologically variable plaques. Furthermore, models trained predominantly on PP-positive cohorts, without inclusion of healthy controls, tend to exhibit local bias and limited generalization capability. This study investigates whether incorporating simple, globally accessible patient-level information (specifically, the radiologist assessed presence or absence of PP) can enhance segmentation performance. We propose a framework that augments a pre-trained segmentation model with a lightweight deep correction module that leverages global diagnostic information to refine local PP segmentation outputs. The results demonstrated the framework’s ability to improve the reliability of traditional segmentation tools for the automated assessment of PP disease. This was achieved by leveraging globally accessible patient-level information, rather than relying on labor-intensive local delineations at the individual plaque level.