PURPOSE: Quantification of metabolite concentrations using MRS requires tissue-dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS-derived concentration estimates. This study investigates the impact and reliability of tissue segmentation on metabolite quantification. METHODS: Three segmentation tools (ANTs, FSL, SPM) were evaluated using an in vivo test-retest MRI/MRS dataset. Voxelwise GM/WM/CSF fractions were applied to compute tissue-corrected total creatine (tCr) concentrations. Linear mixed-effects modeling, variance-component partitioning, and intraclass correlation coefficients (ICCs) quantified tool-, session-, and participant-related variance under permutation scenarios that isolated segmentation- and MRS-related effects. As a benchmark for segmentation performance, comparisons with manually segmented data were conducted across three brain regions. RESULTS: Segmentation tools produced systematically different tissue fractions that propagated into differences in tCr concentration estimates. Variance partitioning attributed 56.8%, 50.0%, and 51.3% of total tCr concentration variability to segmentation tool across the three permutations, with participant-specific factors accounting for 34.7%, 36.2%, and 28.5%, respectively. When segmentation variability was held constant, test-retest reliability was high (ICC > 0.8) but dropped to ∼0.5 when both segmentation and MRS variability varied. Agreement with manual segmentation was region- and tool-dependent, with the lowest agreement in the thalamus. CONCLUSION: Tissue segmentation contributes substantially to the variability in MRS-derived metabolite concentration estimates. These results underscore the need for transparent segmentation reporting and data sharing to ensure reproducibility and cross-study comparability in MRS research.