The performances of two autosegmentation algorithms were evaluated on 28 anonymized pelvic CT scans as a pilot study for the clinical implementation of a semi-automatic workflow. Four organs at risk (OARs), namely the rectum, bladder, and femoral heads, were contoured manually by an expert radiation oncologist (RO)—considered as the ground truth (GT)—and by model-based segmentation (MBS) and deep learning (DL) algorithms. Autocontouring performances were evaluated using a qualitative scoring system, contouring time analysis, and five geometrical indices: the 95th percentile Hausdorff Distance (95HD), Dice Similarity Coefficient (DSC), Surface Dice Similarity Coefficient (SDSC), Added Path Length (APL), and Relative Added Path Length (RAPL). Considering total median value for the four OARs, both MBS and DL showed clinically acceptable results with differences between the two algorithms being not statistically significant for almost all indices. The DL autocontouring algorithm achieved high geometric accuracy, high scores from the ROs, and consistent performances with all validation indices for every OAR. The MBS algorithm achieved high geometric accuracy for the femoral heads and bladder. The DL algorithm required 30 s to contour all the OARs, and the MBS algorithm required 90 s, showing a time gain compared with the manual contours, which took 20 min for each case. The DL autocontouring algorithm obtained promising but preliminary results with every evaluation metric and for every analyzed OAR. The application of the MBS algorithm as the only contouring tool still presents challenges.