This study investigates the variability of quantitative CT (QCT) measurements for interstitial lung disease (ILD) using two same-day CT scans. Sixty-five participants (mean age 68.7 years, 72% men) underwent scans analyzed by deep learning-based texture analysis software (aview Lung Texture, version 1.1.43.7). The software segmented lung parenchyma and ILD features, quantifying them as percentages of total lung volume. Results indicated that the mean absolute variability in fibrosis extent (sum of reticular opacity and honeycombing cysts) between same-day scans was approximately 1%. The 95% limits of agreement (LOA) for absolute and relative differences were −0.9% to 1.0% and −14.8% to 16.1%, respectively. Variability increased significantly between scans with different reconstruction parameters. Multivariable analysis revealed no association between absolute differences and baseline fibrosis extent, but relative differences were negatively associated. QCT results enhanced readers’ specificity in assessing ILD fibrosis stability from 91.7% to 94.6%.