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• A Deep Learning (DL)-assisted contouring tool was evaluated and compared to standard manual contouring when delineating non-small cell lung cancer cases. • Use of the DL-assisted tool reduced active contouring time by 23% compared to the standard manual contouring method. • The average time spent contouring per case decreased from 22 min to 19 min when using the DL-assisted tool. • The DL-assisted tool significantly reduced contour variability in areas of the tumour where clinicians showed the most disagreement, while consensus contours were similar regardless of whether the DL-assisted or manual contouring approach was used. To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring. Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared. Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used. A DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency. [ABSTRACT FROM AUTHOR]