Measurement of total-plaque-area (TPA) is important for determining long term risk for stroke and monitoring carotid plaque progression. Since delineation of carotid plaques is required, a deep learning method can provide automatic plaque segmentations and TPA measurements; however, it requires large datasets and manual annotations for training with unknown performance on new datasets. A UNet++ ensemble algorithm was proposed to segment plaques from 2D carotid ultrasound images, trained on three small datasets (n = 33, 33, 34 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The ensemble was also trained on the entire SPARC dataset and tested with a different dataset (n = 497, Zhongnan Hospital, China). Algorithm and manual segmentations were compared using Dice-similarity-coefficient (DSC), and TPAs were compared using the difference (<inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula>TPA), Pearson correlation coefficient (<italic>r</italic>) and Bland-Altman analyses. Segmentation variability was determined using the intra-class correlation coefficient (ICC) and coefficient-of-variation (CoV). For 44 SPARC subjects, algorithm DSC was 83.3–85.7%, and algorithm TPAs were strongly correlated (<italic>r</italic> = 0.985–0.988; <italic>p</italic> < 0.001) with manual results with marginal biases (0.73–6.75) mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> using the three training datasets. Algorithm ICC for TPAs (ICC = 0.996) was similar to intra- and inter-observer manual results (ICC = 0.977, 0.995). Algorithm CoV = 6.98% for plaque areas was smaller than the inter-observer manual CoV (7.54%). For the Zhongnan dataset, DSC was 88.6% algorithm and manual TPAs were strongly correlated (<italic>r</italic> = 0.972, <italic>p</italic> < 0.001) with <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula>TPA = −0.44<inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>4.05 mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> and ICC = 0.985. The proposed algorithm trained on small datasets and segmented a different dataset without retraining with accuracy and precision that may be useful clinically and for research.