Purpose. We aimed to compare the learning curves of an ultrasound trainee (obstetrics and gynecology resident) and a radiology trainee when assessing pelvic endometriosis. Methods. Consecutive patients with suspected endometriosis were prospectively enrolled in a tertiary center. They underwent an ultrasound and magnetic resonance imaging preoperatively, which was reported according to the International Deep Endometriosis Analysis (IDEA) group consensus. Trainees reported on deep endometriosis (DE), endometriomas, frozen pelvis, and adenomyosis. Using the Kappa agreement, their findings were compared against laparoscopy/histology and expert findings. The learning curve was considered positive when performance improved over time and indeterminate in all other cases. Results. Reports from thirty-five women were divided chronologically into 3 equal blocks to assess the learning curve. For ultrasound, trainee versus expert showed a positive learning curve in overall pelvic DE assessment. There was an excellent agreement for adenomyosis (Kappa=1.00, p=0.09), frozen pelvis (Kappa=0.90, p=0.01), bowel (Kappa=1.00, p=0.01), and bladder DE assessment (Kappa=1.00, p=0.01). Endometrioma and uterosacral ligament assessment showed an indeterminate curve. For radiology, trainee versus expert showed a positive curve when detecting adenomyosis (Kappa=0.42, p=0.09) and bladder DE (Kappa=1.00, p=0.01). The assessment of endometriomas, frozen pelvis, overall pelvic DE, bowel, and uterosacral ligament DE showed indeterminate curve. Agreement between trainees and laparoscopy/histology showed a positive curve for bladder (both) and frozen pelvis (ultrasound only). Conclusion. A positive learning curve can be seen in some areas of pelvic endometriosis mapping after as little as 35 cases, but a bigger caseload is required to demonstrate the curve in full. The ultrasound trainee had positive learning curves in more anatomical locations (bladder, adenomyosis, overall bowel DE, frozen pelvis) than the radiology trainee (bladder, adenomyosis), which could be down to individual factors, differences in training, or the imaging method itself.