BACKGROUND & AIMS: Segmental estimates add specificity to body composition evaluation and could potentially have greater health and function implications than whole-body estimates alone. The aim of this study was to quantify the level of agreement between total and segmental fat mass (FM) and lean soft tissue (LST) estimates from dual-energy x-ray absorptiometry (DXA) and single-frequency bioelectrical impedance analysis (SFBIA). METHODS: Assessments via DXA (GE® Lunar Prodigy) and SFBIA (RJL Systems® Quantum V) were performed in 179 adults (103 F, 76 M; 30% racial/ethnic minorities). Total and segmental FM and LST estimates were compared in the entire sample, females, and males using null hypothesis significance testing (NHST; via paired-samples t-tests), equivalence testing with 5% equivalence regions, Bland-Altman analysis with linear regression, and additional error metrics. RESULTS: In females and the entire sample, all LST variables except LSTARMS exhibited equivalence between methods, despite statistically significant differences via NHST for most variables. In males, only estimates of LSTTOTAL and appendicular lean soft tissue (ALST) were equivalent between methods. LST variables exhibited minimal proportional bias. All FM variables failed to exhibit equivalence, and most FM variables were underestimated by SFBIA. The magnitude of relative errors for FM generally appeared larger in males than females. Proportional bias was observed for FMLEGS and FMARMS, as well as FMTOTAL in females only. ALST estimates were equivalent between DXA and SFBIA in all analyses, did not differ between methods based on NHST, exhibited relatively low errors, and displayed no proportional bias. CONCLUSIONS: In the context of the present study, DXA and SFBIA LST estimates appear to exhibit better overall agreement than FM estimates. Additionally, overall agreement between SFBIA and DXA may be superior in females as compared to males. The relatively strong agreement between ALST estimates indicates potential utility of SFBIA for clinical applications, such as evaluation of sarcopenia. Further investigation into the explanatory physiological (e.g. hydration) and anthropometric (e.g. segment lengths, circumferences, and volumes) variables predicting individual discrepancies between DXA and BIA is warranted.